Can you tell me how you perform data reconciliation?

Data reconciliation is the process of comparing two data sets—typically a source and a target—to ensure they match and to identify discrepancies. The steps are:

1. Define scope – Identify the tables, columns, and time range to compare.
2. Extract snapshots – Pull the relevant data from both systems, often using SQL or data export tools.
3. Normalize data – Convert data types, trim whitespace, and apply consistent formatting.
4. Compare records – Use hash functions, checksums, or row‑by‑row comparison to detect mismatches.
5. Generate a diff report – List added, deleted, or changed rows with key identifiers.
6. Root cause analysis – Investigate why differences exist (e.g., ETL errors, data entry issues, timing differences).
7. Correct and reload – Update the target or source, re‑run the ETL, and re‑validate.
8. Document findings – Keep a reconciliation log for audit and future reference.

Tools such as SQL Server Integration Services (SSIS), Informatica, or custom Python scripts can automate these steps. Regular reconciliation (daily or weekly) ensures data integrity across systems and supports compliance requirements.